bigframes.bigquery.ml.evaluate#
- bigframes.bigquery.ml.evaluate(model: BaseEstimator | str | Series, input_: DataFrame | DataFrame | str | None = None, *, perform_aggregation: bool | None = None, horizon: int | None = None, confidence_level: float | None = None) DataFrame[source]#
Evaluates a BigQuery ML model.
See the BigQuery ML EVALUATE function syntax for additional reference.
- Parameters:
model (bigframes.ml.base.BaseEstimator or str) – The model to evaluate.
input (Union[bigframes.pandas.DataFrame, str], optional) – The DataFrame or query to use for evaluation. If not provided, the evaluation data from training is used.
perform_aggregation (bool, optional) – A BOOL value that indicates the level of evaluation for forecasting accuracy. If you specify TRUE, then the forecasting accuracy is on the time series level. If you specify FALSE, the forecasting accuracy is on the timestamp level. The default value is TRUE.
horizon (int, optional) – An INT64 value that specifies the number of forecasted time points against which the evaluation metrics are computed. The default value is the horizon value specified in the CREATE MODEL statement for the time series model, or 1000 if unspecified. When evaluating multiple time series at the same time, this parameter applies to each time series.
confidence_level (float, optional) – A FLOAT64 value that specifies the percentage of the future values that fall in the prediction interval. The default value is 0.95. The valid input range is
[0, 1).
- Returns:
The evaluation results.
- Return type: